{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linearization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This jupyter notebook is used to visualize (not verify) the linearization capabilities in RECONR. The linearziation is rather course because we used a relative tolerance of 0.1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotDifference(refX, refY, linX, linY, MT):\n",
    "    fig, ax = pyplot.subplots()\n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.plot(linX, linY, \"-o\", color=\"blue\", label=\"linearized\")\n",
    "    ax.plot(refX, refY, \"o\", color=\"red\", markerfacecolor=\"white\", label=\"initial\")\n",
    "    ax.set_title(\"MT={}\".format(MT))\n",
    "    ax.legend()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MT=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refX = [1e-05,2e-05,750000,1.9e+07,1.95e+07,2e+07]\n",
    "refY = [1,11.829,3.34739e-05,2.75176e-05,2.7313e-05,2.71079e-05]\n",
    "linX = [1e-05,2e-05,2e-05,750000,750000,1.9e+07,1.9e+07,1.95e+07,1.95e+07,2e+07,2e+07]\n",
    "linY = [1,1,11.829,11.829,3.34739e-05,3.34739e-05,2.75176e-05,2.75176e-05,2.7313e-05,2.7313e-05,2.71079e-05]\n",
    "\n",
    "len(linX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plotDifference(refX, refY, linX, linY, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MT=16 (n,2n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refX = [1e-05,2e-05,750000,1.9e+07,1.95e+07,2e+07]\n",
    "refY = [16,11.829,3.34739e-05,2.75176e-05,2.7313e-05,2.71079e-05]\n",
    "linX = [1e-05,1.25e-05,1.5e-05,2e-05,2e-05,2.5457e-05,3.09139e-05,4.18279e-05,5.27418e-05,6.36557e-05,8.54836e-05,0.000107311,0.000150967,0.000194623,0.000281934,0.000369246,0.000718492,0.00141698,0.00281397,0.00560794,0.0111959,0.0223717,0.0447235,0.089427,0.178834,0.357648,0.715276,1.43053,2.86104,5.72207,11.4441,22.8882,45.7764,91.5528,183.105,366.211,732.422,1464.84,2929.69,5859.38,11718.8,23437.5,46875,93750,187500,375000,750000,750000,1.9e+07,1.9e+07,1.95e+07,1.95e+07,2e+07]\n",
    "linY = [16,14.5175,13.4088,11.829,11.829,10.4225,9.41266,8.0318,7.11182,6.4435,5.51996,4.89908,4.09575,3.5847,2.95121,2.56166,1.80645,1.26493,0.882532,0.614596,0.427607,0.29737,0.206751,0.143729,0.0999125,0.0694513,0.0482764,0.0335573,0.0233258,0.0162138,0.0112703,0.00783398,0.00544542,0.00378512,0.00263104,0.00182884,0.00127123,0.000883634,0.000614215,0.000426942,0.000296768,0.000206284,0.000143388,9.96694e-05,6.92804e-05,4.81569e-05,3.34739e-05,3.34739e-05,2.75176e-05,2.75176e-05,2.7313e-05,2.7313e-05,2.71079e-05]\n",
    "len(linX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ALUHQT/t9i0nYFbqPPQa/+hW88QaccALccouqeESkde2xhn80B87kIQj0XwXuBx40s3OA3yZ6sZlNB6YDlCdazygyU6aEV+Sccgr8y7/Az352YExVPCKSSHsE/LBbf7i7bwWuae3F7v6wmX0KTOrYseNXMj67AjJkSPiNVmJ99hXwRSReeyyt1AL94p6XAQlqTcLl6x2vciGsBw+oikdEmmuPgL8UONbMBppZR+AyYGEqOyjEbpntJdGq16GHBlfqiojEpFuWWQ28DRxnZrVmNtXd9wA3Ai8By4Gn3f39VParM/zkhVXxdOgA27fDiSfCT36ihK6IBCJ5pa2ZTQImDRo0aNqKFStyPZ3IW7CgeVuGgQPhvPOgvv7gbbt0Cap+tL4vUrgSXWkbyYAfo9YK6VGffZHilFf98LWGnxnqsy8i8SIZ8LWGnxmJErrdusHOndmdi4jkXiQDvmRGoj77W7bAuHHwwQfB+r+SuiLFIZL98OOStrmeSl5L1Ge/Rw+45hoYPjy4cCt2Vy1dpStS2JS0LVKffALHHBO+tKOkrkh+y6ukrbS/o49OfL9cJXVFClMkA76qdLIjUVK3X7/wcRHJb5EM+KrSyY6wpC5A166JSzpFJH9FMuBLdjTttV9eDtdeGyRvTzgBfvhDVfCIFBIlbaWZDz6AiRObr+WrLYNIflDSVpJ23HHh47E++yKSnyIZ8JW0zb01a8LHVcEjkr8iGfCVtM29lvrsb9qU3bmISGZEMuBL7rXUZ3/kSLj9diV0RfKNAr6EalrB078/zJsHb78NW7fCHXcE1TzuB1oyKOiLRJuqdCRl5eXha/xqySASDarSkYzRjdNF8lMkA76qdKItUUK3a9dgjV9EoimSAV9VOtGWqM9+QwOMHQvvp3TLehHJlkgGfIm2sIRuVRW88AKsWwdjxsDUqcG4qnhEokNJW8mozz6DM8+E9947eFxtGUSyR0lbyYovfQm++KL5uNoyiOSeAr5knNoyiERT1gK+mf2zmc0xs2ey9Z6SG4mqeDp2TPzLQETaX1IB38zmmtl6M3uvyXiFmX1gZivNbEZL+3D3D919ajqTlfwQVsXTsWPw8YQT4HvfU1sGkVxI9gy/CqiIHzCzUuAh4CxgCFBpZkPMbLiZPd/k0Tejs5ZIC6vimTs3SOQedhjce6/aMojkwiHJbOTui81sQJPhscBKd/8QwMyeAs5397uAczM5Sck/U6aEV+Ts3dt8LJbQVQWPSPtKZw3/aCB+Rba2cSyUmfUys9nAiWb27y1sN93Masyspq6uLo3pSRQpoSuSO+kEfAsZS1jU7+717n6dux/T+FdAou0edvfR7j66T58+aUxPoihRQrdzZ/j88+zORaTYpBPwa4F+cc/LgLXpTSegXjqFK1Gf/Z07g4Tu4sXBer6SuiKZl07AXwoca2YDzawjcBmwMDPTkkKVqM/+0qXB3bTGj4drrlFSV6Q9JNVawcyqgQlAb2AdcJu7zzGzs4F7gVJgrrvPyuTk1FqhuDQ0BFfqbt3a/HPqtS+SvEStFSLZS8fMJgGTBg0aNG3FihW5no5kUUlJcGbflBns25f9+Yjko7zqpaP2yMUrUVK3rCy78xApRJEM+FK8wpK6ENTv/+Uv2Z+PSCGJZMBXlU7xCkvqzpgRLOeMHQtXX60++yJtFck1/BglbSWmri7os//nPx88rj77Is3l1Rq+zvClqT59YOPG5uPqsy+SvEgGfCVtJYzaMoikJ5IBXyRMogqeDh1Uoy+SDAV8yRuJ+uyXlMDIkfD002rLINKSSAZ8reFLmER99pctg8GD4dJL4aqr1JZBJBFV6UhB2L0bevcOv4G62jJIscmrKh2RVHXoAFu2hH9OSV2RgAK+FIxESd2jE96WR6S4RDLgaw1f2iJRW4YtW+DVV7M/H5GoiWTAVx2+tEVYUvfOO+HII+GMM+D889WWQYqbkrZS8LZuhUmTYNGig8fVlkEKlZK2UrS6doUPP2w+rrYMUmwU8KUoJKrUUQWPFBMFfCkKiSp4Skvhf/83u3MRyZVIBnxV6UimhVXwdOoE3brBuHFw771qyyCFL5IBX1U6kmlhFTxz5sDKlXDWWfC978GVV6otgxQ2VelI0XOHXr3C++2rLYPkI1XpiCRgBps2hX9OSV0pJAr4IiRO6h55ZHbnIdKeFPBFSNyWob4+WMdXQlcKwSG5noBIFMSutp05M1jGKS+H738f/vu/4YorgvLNvXuDbWIJ3fjXieSDrCZtzWwycA7QF3jI3V9uaXslbSXX9uwJ+uyHVQgroStRlXbS1szmmtl6M3uvyXiFmX1gZivNbEZL+3D337j7NOBq4NJk31skVw45JPymKqCEruSfVNbwq4CK+AEzKwUeAs4ChgCVZjbEzIab2fNNHn3jXvrjxteJRJ767EuhSDrgu/ti4PMmw2OBle7+obvvAp4Cznf3v7r7uU0e6y3wU+BFd/9T2PuY2XQzqzGzmrq6urYel0jGJErobt4ML72U/fmItFW6VTpHA2vintc2jiVyE3AGcJGZXRe2gbs/7O6j3X10nz590pyeSPrCrtK9++7gY0UFnHOO+uxLfki3SsdCxhJmgd39fuD+VndqNgmYNGjQoDSmJpI5U6Y0r8j5t3+D886DF144MKYKHomydM/wa4F+cc/LgLVp7lO9dCQvHHoorFjRfFx99iWq0g34S4FjzWygmXUELgMWpjspdcuUfKE++5JPUinLrAbeBo4zs1ozm+rue4AbgZeA5cDT7v5++0xVJHpa6rO/dGl25yLSmlSqdCrd/Uh37+DuZe4+p3H8BXf/srsf4+6zMjEpLelIvkjUZ79HD/ja1+Cee2D+fLVlkGhQawWRNIS1ZJg1C84+O0je/uhHQaDfty/YTkldyaVI9sOPq9KZtiIsKyaSB9yDtgyfN716BbVlkPaVV/3wtaQjhcAs/KYqoKSu5EYkA75IoVCffYmSSAZ8lWVKoUjUlmHDBqiqCpZ9RLIlkgFfSzpSKMLaMjzwQFDBc801cPLJwV8BquCRbIhk0jZG/fClUO3dC5WVwQ1W4nXpEvyCUAWPpCOvkrZa0pFCV1oK//M/zcfVlkHaUyQDvpZ0pBioLYNkWyQDvkgxSFTBYwa/+1125yLFQQFfJEfCKng6dw7upHXuufDd78Jjj6ktg2ROJFsrqB++FINEbRkuvBBmzID77gvO9mN1FWrLIOlSlY5IRPXtC2F3+VRbBmlNXlXpiEhwcVYYJXWlrRTwRSIqUVK3b9/szkMKhwK+SESFJXXNYP364HNPPKGErqQmkklbEQlP6v74x/DKK8FH9dmXVEUyaat++CKJuUOfPlBf3/xzSugK5FnSVlfaiiRmFn5TFVBCV1oWyYAvIi1LlND90peyOw/JLwr4InmopT77jzyiPvsSTgFfJA+F9dl/8EEYPz5I3l5ySfB5VfFIvEgmbWN0pa1Iavbtg5//PGjNAAef6avXfvHIq6StiLRNSQnccgsccUTzZR312pesBXwzG2xms83sGTO7PlvvK1KM1q0LH1cVT3FLKuCb2VwzW29m7zUZrzCzD8xspZnNaGkf7r7c3a8DLgGa/akhIpmTqIqnd+/szkOiJdkz/CqgIn7AzEqBh4CzgCFApZkNMbPhZvZ8k0ffxtecBywB/pCxIxCRZhK1Zairg+98B+bNU0K3GCXVWsHdF5vZgCbDY4GV7v4hgJk9BZzv7ncB5ybYz0JgoZn9DngybBszmw5MByhPdJoiIi0Ka8twxx3w17/Cf/2X+uwXq6SrdBoD/vPuPqzx+UVAhbtf2/j8m8BX3f3GBK+fAFwAdAL+4u4PtfaeqtIRybwjjggasDWltgyFI1GVTjrN0yxkLOFvD3d/DXgtqR3rjlci7SbspiqghG4xSKdKpxboF/e8DFib3nQC6qUj0n7UZ794pRPwlwLHmtlAM+sIXAYszMSkzGySmT28efPmTOxOROIkSuiuWwe33w579uRkWpIFyZZlVgNvA8eZWa2ZTXX3PcCNwEvAcuBpd38/E5PSGb5I+wlry/DII3DllUFi9xvfgPvvVxVPIYpkawX1wxfJjfnz4dprYefOg8fVliG/JEraRjLgx6hKRyT7jj4a1oZk41TFkz/yqpeO1vBFcufTT8PHVcWT/yIZ8LWGL5I7iap4DjtMffbzXSQDvojkTlgVT0kJbNwI//qv8KtfKaGbr9K58Krd6MIrkdwJa8vwn/8ZXLD1wx/CwoVqy5CvlLQVkaQdeSR89lnzcSV0oyWvkrYiEk3qs5/fIhnwVaUjEk2JErq9emV3HtI2kQz4qtIRiaZECd0NG2DaNNi6NTfzkuREMuCLSDSFtWWYNy+4afqcOTB6NNx1l6p4okpJWxHJiFdegQsvhC++OHhcbRmyL6+StlrDF8k/Z5wBPXo0H9+2LSjxlNyLZMDXGr5Ifvrkk/BxVfFEQyQDvojkp0RVPN27w+7d2Z2LNKeALyIZE1bFU1oarOufeirce68SurkUydYKIpKfwtoyzJoFhxwCV18Nf/zjgW3VliH7VKUjIlmhPvvZoyodEckp9dnPvUgGfFXpiBQe9dnPvUgGfBEpPIkSuhs3wrnnwuzZSui2NyVtRSQrEiV0N26E734XXnjhwLZK6LYPJW1FJOeOOip8jV8J3bbJq6StiBSXsJuqgBK6maaALyI5pz772ZHVgG9mXc3sHTM7N5vvKyLR1lKf/WuugYaG3Myr0CQV8M1srpmtN7P3moxXmNkHZrbSzGYksasfAU+3ZaIiUrjC+uxXVcF//Ac8/jiMGhX8UlAVT3qSStqa2alAA/C4uw9rHCsF/g6cCdQCS4FKoBS4q8kuvgWMAHoDnYEN7v58a++rpK2IvP46TJ4MmzYdPK4++4klStomVZbp7ovNbECT4bHASnf/sPENngLOd/e7gGZLNmZ2GtAVGAJsN7MX3H1fSkchIkVn/Hjo1q15wI/12VfAT146dfhHA2vintcCX020sbvPBDCzqwnO8EODvZlNB6YDlCfK5IhIUVGf/cxIJ+BbyFir60PuXtXK5x82s0+BSR07dvxKG+cmIgWkvDy4GKup7t1h1y7o2DH7c8pH6VTp1AL94p6XASG98FKnXjoiEi+siueQQ4I++yefDL/4hRK6yUgn4C8FjjWzgWbWEbgMWJiJSalbpojES1TF8+tfw/vvww9+EPwF4H6gLYOCfnPJVulUAxMIqmzWAbe5+xwzOxu4l6AyZ667z8rk5FSlIyKtKSsLX+Mv5rYMiap0ItlLx8wmAZMGDRo0bcWKFbmejohEWElJeHtlM9hXpHWAedVLR2v4IpKsRMV8PXsWb8BPJJIBX2v4IpKsRH32N22CiorEjdmKUSQDvs7wRSRZYQndxx4Lbqjyxhtwwglwyy2q4oGIruHHKGkrIul4//3gLL+29uDxQm/LkFdr+FrSEZFMGDo0OOtvKtaWodhEMuBrSUdEMqXp2X1MMbZliGTAFxHJlERVPIceGlypW0wiGfC1pCMimRJWxdOhA2zfDieeCP/3q9Ws7jGMfSWlrO4xjLkTq3Mz0SyIZMDXko6IZEpYFc+8eUEFz6mfVDN99Uz6P/cAJTt30P+5B7j0LzMLNuirSkdEitbqHsPo/9wDcNppBwYXLWLVeTfxwk/fo08f6NMH+vYNPv7TPwU1/lGXV60VYhTwRaQ97SsppWTnjmCNJ2b3bvZ16kyp7222fUlJcGP12C+ARB9j/z788OA1yXqtcwXjO70NW7ZA9+68vvMkJuz4fcrHldYdr0RECtGaboPpv2TJwWf4S5awpttgPlsB69dDXV3ij+++G3zcuDF8/6Wl0Lt3y78cYh8/PaGC8Yf/GXvyN3DKKbBkCeMvv5zXOle0KeiHiWTAj2ueluupiEgB+8O4mVxaOZWu1XP2B9mtlVP5w7hZfOsIOOKI5Pazezds2NDyL4f16+FPfwo+htWj7OvxdhDsY798TjsNe/JJxk+enLHj1ZKOiBS1uROrOf2Ps+jXsJw13Qbzh3Ez+dbLle36njt3Nv8Fcfk3S7CdO5stL3mnTliKXeC0hi8iEmHesyf2m980SyD75MlYiiXqedVaQUSk2Ly+8yT88sth0aJgjeILzkQAAAORSURBVGjRIvzyy3l950kZe49IruGLiBSbCTt+H1TpTJ6cdpVOIgr4IiIR0TS4T8jw/iO5pKPWCiIimRfJgK/WCiIimRfJgC8iIpmngC8iUiQU8EVEikSkL7wyszpgda7n0YLewIZcTyJNhXAMUBjHUQjHAIVxHPl+DP3dvU/TwUgH/Kgzs5qwq9nySSEcAxTGcRTCMUBhHEchHEMYLemIiBQJBXwRkSKhgJ+eh3M9gQwohGOAwjiOQjgGKIzjKIRjaEZr+CIiRUJn+CIiRUIBX0SkSCjgi4gUCQX8dmRmXc3sHTM7N9dzaQszm2xmj5jZc2Y2MdfzSVbj1/2xxrlPyfV82ipfv/5N5fvPAYCZlZjZLDN7wMyuyvV82koBP4SZzTWz9Wb2XpPxCjP7wMxWmtmMJHb1I+Dp9pllyzJxDO7+G3efBlwNXNqO021VisdzAfBM49zPy/pkW5DKcUTp6x+vDd9bOfs5aEmKx3E+cDSwG6jN9lwzRQE/XBVQET9gZqXAQ8BZwBCg0syGmNlwM3u+yaOvmZ0BLAPWZXvyjapI8xjiXvrjxtflUhVJHg9QBqxp3GxvFueYjCqSP46YKHz941WR/PdWrn8OWlJF8v8XxwFvu/v3geuzPM+M0R2vQrj7YjMb0GR4LLDS3T8EMLOngPPd/S6g2Z+qZnYa0JXgm2a7mb3g7qndej4NGToGA+4GXnT3P7XvjFuWyvEQnIGVAe8SsZOaVI7DzJYTka9/vBT/L7qRw5+DlqR4HGuAXY3bRO0kImkK+Mk7mgNnjRAEla8m2tjdZwKY2dXAhoh8k6d0DMBNwBlATzMb5O6z23NybZDoeO4HHjSzc4Df5mJiKUp0HFH/+scLPQZ3vxEi93PQkkT/F/cBD5jZ14HFuZhYJijgJ89Cxlq9as3dqzI/lTZL6Rjc/X6C4BlVocfj7luBa7I9mTQkOo6of/3jtfi9FbGfg5Yk+r/YBkzN9mQyLVJ/7kZcLdAv7nkZsDZHc2mrQjiGeIVyPIVwHIVwDFA4xxFKAT95S4FjzWygmXUELgMW5nhOqSqEY4hXKMdTCMdRCMcAhXMc4dxdjyYPoBr4lAMlWFMbx88G/g78A5iZ63kW+jEU4vEUwnEUwjEU0nGk8lDzNBGRIqElHRGRIqGALyJSJBTwRUSKhAK+iEiRUMAXESkSCvgiIkVCAV9EpEgo4IuIFAkFfBGRIvH/AUdceApDMyzHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plotDifference(refX, refY,linX, linY, 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MT=18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refX = [100000,150000,750000,1.9e+07,1.95e+07,2e+07]\n",
    "refY = [18,11.829,3.34739e-05,2.75176e-05,2.7313e-05,2.71079e-05]\n",
    "linX = [100000,150000,150000,750000,750000,1.9e+07,1.95e+07,2e+07]\n",
    "linY = [18,18,11.829,11.829,3.34739e-05,2.75176e-05,2.7313e-05,2.71079e-05]\n",
    "\n",
    "len(linX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plotDifference(refX, refY,linX, linY, 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MT=102"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refX = [1e-05,2e-05,750000,1.9e+07,1.95e+07,2e+07]\n",
    "refY = [102,11.829,3.34739e-05,2.75176e-05,2.7313e-05,2.71079e-05]\n",
    "linX = [1e-05,1.0625e-05,1.125e-05,1.1875e-05,1.25e-05,1.3125e-05,1.375e-05,1.4375e-05,1.5e-05,1.5625e-05,1.625e-05,1.75e-05,1.875e-05,2e-05,2e-05,6.36557e-05,0.000107311,0.000194623,0.000369246,0.000718492,0.00141698,0.00281397,0.00560794,0.0111959,0.0223717,0.0447235,0.089427,0.178834,0.357648,0.715276,1.43053,2.86104,5.72207,11.4441,22.8882,45.7764,91.5528,183.105,366.211,732.422,1464.84,2929.69,5859.38,11718.8,23437.5,46875,93750,187500,375000,750000,1.9e+07,1.9e+07,1.95e+07,1.95e+07,2e+07]\n",
    "linY = [102,94.1134,86.6777,79.6441,72.9714,66.6243,60.5726,54.7899,49.2533,43.9428,38.8406,29.2,20.2247,11.829,11.829,11.2665,11.0128,10.7235,10.4124,10.089,9.75904,9.42573,9.0907,8.75481,8.41849,8.08195,7.7453,7.4086,7.07187,6.73513,6.39838,6.06163,5.72488,5.38812,5.05137,4.71461,4.37786,4.0411,3.70435,3.36759,3.03083,2.69408,2.35732,2.02057,1.68381,1.34706,1.0103,0.673545,0.336789,3.34739e-05,2.75176e-05,2.75176e-05,2.7313e-05,2.7313e-05,2.71079e-05]\n",
    "len(linX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plotDifference(refX, refY,linX, linY, 102)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reconstructed Resonances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SLBW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "refGrid = [0.00001, 0.9860692, 1.0253, 1.0645308, 2.9860692, 3.0253, 3.0645308, 7.5]\n",
    "refEnergies = [1e-05,    0.246525, 0.49304,  0.616297, 0.739554, 0.801183, 0.862812,  \n",
    "          0.893626, 0.92444,  0.955255, 0.986069, 1.0253,   1.06453,  1.09455,   \n",
    "          1.12458,  1.1546,   1.18463,  1.24468,  1.30472,  1.42482,  1.54492,   \n",
    "          1.78511,  2.0253,   2.26549,  2.50568,  2.62578,  2.74588,  2.80592,   \n",
    "          2.86597,  2.896,    2.92602,  2.95605,  2.98607,  3.0253,   3.06453,   \n",
    "          3.08186,  3.09918,  3.13384,  3.16849,  3.20314,  3.27244,  3.34175,   \n",
    "          3.48036,  3.61896,  3.89618,  4.1734,   4.72783,  5.28227,  6.39113,   \n",
    "          7.5]\n",
    "refElastic =[\n",
    "          0.12506,    0.142374,  0.239419,  0.376941,  0.731633,  1.15853,     \n",
    "          2.12402,    3.132,     5.03349,   9.1272,    19.0871,   38.1473,     \n",
    "          19.0819,    9.28208,   5.16956,   3.23402,   2.19952,   1.20137,     \n",
    "          0.756675,   0.38362,   0.235867,  0.12328,   0.0861085, 0.0790813,   \n",
    "          0.10576,    0.151698,  0.274666,  0.423839,  0.760705,  1.11065,     \n",
    "          1.76604,    3.15938,   6.48055,   12.9402,   6.47874,   4.21545,     \n",
    "          2.85963,    1.51044,   0.9191,    0.616025,  0.333129,  0.21033,     \n",
    "          0.108652,   0.0682899, 0.0363148, 0.0237135, 0.0133756, 0.00901362,  \n",
    "          0.00516115, 0.00343646 ]\n",
    "refCapture = [\n",
    "          6850.24, 49.8161, 58.9968, 82.7983, 146.211, 222.108, 391.906,   \n",
    "          567.549, 896.41,  1598.51, 3289.5,  6446.72, 3165.14, 1518.74,   \n",
    "          834.808, 515.696, 346.513, 185.012, 114.139, 55.8541, 33.4273,   \n",
    "          17.0292, 12.114,  11.835,  17.1509, 25.4053, 46.9056, 72.6816,   \n",
    "          130.496, 190.301, 301.974, 538.661, 1100.86, 2185.28, 1086.47,   \n",
    "          704.507, 476.198, 249.594, 150.619, 100.063, 53.0942, 32.8511,   \n",
    "          16.2643, 9.79166, 4.79638, 2.91043, 1.45495, 0.89217, 0.443622,  \n",
    "          0.265993 ]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f93881c40d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "    fig, ax = pyplot.subplots()\n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_yscale(\"log\")\n",
    "\n",
    "    ax.plot(refEnergies, refElastic, '.r-', label=\"elastic\")\n",
    "    ax.plot(refEnergies, refCapture, '.b-', label=\"capture\")\n",
    "    ax.set_title(\"SLBW\")\n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reich-Moore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "refGrid = [\n",
    "        0.98596, 1.06389, 1.1389, 1.21391, 2.02109, 2.0361, 2.05111, \n",
    "        2.74827, 2.7767, 2.80513, 3.14517, 3.1566, 3.16803, 3.60111, \n",
    "        3.6208, 3.64049, 4.8336, 4.8508, 4.868, 5.24932, 5.4497, 5.5]\n",
    "refEnergies = [\n",
    "          0.98596,  1.02493,  1.06389,  1.1389,   1.21391,  1.26436,  1.31481,  \n",
    "          1.4157,   1.6175,   1.8193,   1.86974,  1.92019,  1.94542,  1.97064,  \n",
    "          1.98325,  1.99587,  2.00848,  2.02109,  2.0361,   2.05111,  2.0729,   \n",
    "          2.09468,  2.11647,  2.13826,  2.18183,  2.2254,   2.31255,  2.39969,  \n",
    "          2.57398,  2.66113,  2.7047,   2.74827,  2.7767,   2.80513,  2.89014,  \n",
    "          2.97515,  3.01765,  3.06016,  3.14517,  3.1566,   3.16803,  3.22216,  \n",
    "          3.2763,   3.33044,  3.38457,  3.43871,  3.49284,  3.51991,  3.54697,  \n",
    "          3.57404,  3.60111,  3.6208,   3.64049,  3.65913,  3.67777,  3.69642,  \n",
    "          3.71506,  3.75234,  3.78963,  3.8642,   3.93877,  4.08791,  4.23705,  \n",
    "          4.38618,  4.53532,  4.60989,  4.68446,  4.72175,  4.75903,  4.77767,  \n",
    "          4.79632,  4.81496,  4.82428,  4.8336,   4.8508,   4.868,    4.87992,  \n",
    "          4.89183,  4.90375,  4.91567,  4.9395,   4.96333,  5.01099,  5.05866,  \n",
    "          5.15399,  5.24932,  5.4497,   5.5 ]\n",
    "refElastic = [\n",
    "          14.5826, 14.5327, 14.5131, 14.8464, 15.0201, 14.8999, 14.7925,\n",
    "          14.6392, 14.4366, 14.2619, 14.2091, 14.1403, 14.0929, 14.0281,\n",
    "          13.9863, 13.939, 13.9007, 13.9354, 14.2386, 14.5251, 14.5097,\n",
    "          14.4214, 14.3556, 14.3078, 14.241, 14.1931, 14.1203, 14.0599,\n",
    "          13.9471, 13.8813, 13.8362, 13.7759, 13.7421, 13.7296, 13.7054,\n",
    "          13.6351, 13.5874, 13.539, 13.6968, 13.7725, 13.8435, 13.9331,\n",
    "          13.8284, 13.7176, 13.6097, 13.4893, 13.3362, 13.2434, 13.156,\n",
    "          13.1662, 13.6013, 14.2867, 14.7618, 14.821, 14.7118, 14.5741,\n",
    "          14.4504, 14.2624, 14.1328, 13.9657, 13.8571, 13.7056, 13.5815,\n",
    "          13.4505, 13.2714, 13.1313, 12.8992, 12.7039, 12.3859, 12.1396,\n",
    "          11.7954, 11.3532, 11.1917, 11.3625, 14.4855, 16.9067, 16.5402,\n",
    "          15.9963, 15.5563, 15.2253, 14.781, 14.5025, 14.1737, 13.9824,\n",
    "          13.7587, 13.6287, 13.5656, 13.5755 ]\n",
    "refCapture = [\n",
    "          9.93351, 13.3482, 19.639, 36.16, 19.178, 10.8929, 7.09064,\n",
    "          4.13228, 2.63351, 2.91457, 3.59415, 5.41459, 7.59086, 12.3436,\n",
    "          17.0365, 25.1773, 39.9831, 65.0758, 88.8534, 64.4467, 27.7593,\n",
    "          14.1011, 8.62062, 5.99604, 3.69314, 2.75031, 2.01499, 1.76788,\n",
    "          1.8389, 2.35618, 3.04173, 4.17966, 4.6298, 4.32927, 3.24947,\n",
    "          4.04808, 5.35192, 8.01053, 20.1129, 20.6294, 20.1278, 11.7661,\n",
    "          6.9162, 5.37652, 5.41327, 6.87542, 11.1856, 15.9875, 25.0851,\n",
    "          42.9939, 71.056, 79.9209, 64.9016, 44.9732, 30.3828, 21.1713,\n",
    "          15.4037, 9.17284, 6.16688, 3.53898, 2.48523, 1.73358, 1.61699,\n",
    "          1.90918, 3.03441, 4.5798, 8.60412, 13.5732, 25.2965, 38.1737,\n",
    "          64.0024, 125.221, 188.316, 292.8, 499.675, 301.393, 171.423,\n",
    "          103.625, 67.8095, 47.4078, 26.7679, 17.2455, 9.11788, 5.90234,\n",
    "          3.57183, 2.98869, 3.11492, 3.01658 ]\n",
    "refFission = [\n",
    "          64.155, 73.7218, 89.705, 111.73, 49.7073, 30.5546, 23.3012,\n",
    "          18.4774, 15.6896, 14.3587, 14.2416, 14.443, 14.8764, 15.9617,\n",
    "          17.0766, 19.0255, 22.548, 28.3832, 33.3652, 26.8838, 18.0184,\n",
    "          14.7916, 13.4737, 12.8033, 12.1121, 11.7105, 11.1305, 10.5992,\n",
    "          9.04656, 7.30333, 6.11291, 6.59994, 9.8862, 14.2276, 19.2682,\n",
    "          22.5998, 26.3267, 33.37, 59.2035, 58.8323, 56.0236, 32.6866,\n",
    "          21.8327, 19.1471, 19.9451, 23.6452, 32.5672, 41.4005, 56.5703,\n",
    "          82.8037, 114.893, 112.824, 79.6278, 48.2594, 28.5979, 17.619,\n",
    "          11.493, 5.87904, 3.8114, 2.71883, 2.68868, 3.11531, 3.59612,\n",
    "          4.12546, 4.87729, 5.48662, 6.5506, 7.53293, 9.40118, 11.1709,\n",
    "          14.3614, 21.1263, 27.5253, 37.3192, 51.8651, 28.7732, 17.0467,\n",
    "          11.5986, 9.04432, 7.7853, 6.84385, 6.71141, 7.2079, 8.12153,\n",
    "          10.8557, 15.0913, 24.6041, 24.191 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f93b9864190>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pyplot.subplots()\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xlabel(\"Neutron Energy (eV)\")\n",
    "ax.set_ylabel(\"Cross Sections (b)\")\n",
    "\n",
    "ax.plot(refEnergies, refElastic, '.r-', label=\"elastic\")\n",
    "ax.plot(refEnergies, refCapture, '.b-', label=\"capture\")\n",
    "ax.plot(refEnergies, refFission, '.g-', label=\"fission\")\n",
    "ax.set_title(\"SLBW\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing MAT=2631, MF=3, MT=102\n",
    "This data came from Legacy NJOY's reconstruction of the cross section data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Only 1 *l*-value\n",
    "I (@jlconlin) removed all but the first *l*-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdc114f8fd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [ 1.000000E-5, 1.500000E-5, 2.000000E-5, 2.750000E-5, 3.500000E-5, 5.000000E-5, 7.500000E-5, 1.000000E-4, 1.500000E-4, 2.000000E-4, 2.750000E-4, 3.500000E-4, 5.000000E-4, 7.500000E-4, 1.000000E-3, 1.500000E-3, 2.000000E-3, 2.750000E-3, 3.500000E-3, 5.000000E-3, 7.500000E-3, 1.000000E-2, 1.500000E-2, 2.000000E-2, 2.530000E-2, 3.765000E-2, 5.000000E-2, 7.500000E-2, 1.000000E-1, 1.500000E-1, 2.000000E-1, 2.750000E-1, 3.500000E-1, 4.139999E-1, 4.140001E-1, 4.140008E-1, 4.140015E-1, 4.140028E-1, 4.140054E-1, 4.140106E-1, 4.140211E-1, 4.140421E-1, 4.140841E-1, 4.141681E-1, 4.143361E-1, 4.146720E-1, 4.153439E-1, 4.166876E-1, 4.193751E-1, 4.247501E-1, 4.355001E-1, 4.570001E-1, 5.000000E-1, 6.250000E-1, 7.500000E-1, 1.000000E+0, 1.500000E+0, 2.000000E+0, 2.750000E+0, 3.500000E+0, 5.000000E+0, 7.500000E+0, 1.000000E+1, 1.500000E+1, 2.000000E+1, 2.750000E+1, 3.500000E+1, 5.000000E+1, 7.500000E+1, 1.000000E+2, 1.500000E+2, 2.000000E+2, 2.750000E+2, 3.500000E+2, 5.000000E+2, 7.500000E+2, 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4.447360E-4, 4.397440E-4, 4.360000E-4, 4.347160E-4, 4.312000E-4, 4.240000E-4, 4.203640E-4, 4.140880E-4, 4.088200E-4, 4.056280E-4, 4.034800E-4, 4.000000E-4, 3.987360E-4, 3.965680E-4, 3.951120E-4, 3.936560E-4, 3.896000E-4, 3.862720E-4, 3.776000E-4, 3.702960E-4, 3.680000E-4, 3.663920E-4, 3.600000E-4, 3.520160E-4, 3.503840E-4, 3.402240E-4, 3.394400E-4, 3.304000E-4, 3.200000E-4, 3.179600E-4, 3.179500E-4, 3.157120E-4, 3.136000E-4, 3.100000E-4, 3.066680E-4, 3.000000E-4, 3.050000E-4, 3.062500E-4, 3.075000E-4, 3.087500E-4, 3.100000E-4, 3.175000E-4, 3.209200E-4, 3.250000E-4, 3.316000E-4, 3.400000E-4, 3.721600E-4, 3.800000E-4, 3.880000E-4, 3.960000E-4, 4.040000E-4, 4.120000E-4, 4.200000E-4, 4.400200E-4, 4.480000E-4, 4.760000E-4, 4.900000E-4, 5.040000E-4, 5.320000E-4, 5.600000E-4, 6.280000E-4, 6.450000E-4, 6.960000E-4, 7.300000E-4, 7.450000E-4, 7.540000E-4, 7.600000E-4, 7.750000E-4, 7.900000E-4, 8.020000E-4, 8.200000E-4, 8.500000E-4, 8.900000E-4, 9.300000E-4, 9.400000E-4, 9.500000E-4, 9.250000E-4, 9.000000E-4, 8.750000E-4, 8.500000E-4, 8.250000E-4, 8.000000E-4, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 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0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0 ]\n",
    "\n",
    "\n",
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "ax.plot(x, y, \"-\", lw=0.5, label=\"legacy\")\n",
    "ax.plot(bx, by, \"-\", lw=0.5, label=\"modern\")\n",
    "\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xlabel(\"Neutron Energy (eV)\")\n",
    "ax.set_ylabel(\"Cross Sections (b)\")\n",
    "#ax.set_xlim( 1E-5, 1E4)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Only 2 *l*-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdbc0ee1610>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
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    "\n",
    "\n",
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "#ax.plot(x, y, \"-\", lw=0.5, label=\"legacy\")\n",
    "ax.plot(bx, by, \"-\", lw=0.5, label=\"modern\")\n",
    "\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xlabel(\"Neutron Energy (eV)\")\n",
    "ax.set_ylabel(\"Cross Sections (b)\")\n",
    "#ax.set_xlim( 1E-5, 1E4)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single resonance test\n",
    "This utilizes a single resonance file that @whaeck created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdbd0d0d950>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [ 1.000000E-5, 1.500000E-5, 2.000000E-5, 2.750000E-5, 3.500000E-5, 5.000000E-5, 7.500000E-5, 1.000000E-4, 1.500000E-4, 2.000000E-4, 2.750000E-4, 3.500000E-4, 5.000000E-4, 7.500000E-4, 1.000000E-3, 1.500000E-3, 2.000000E-3, 2.750000E-3, 3.500000E-3, 5.000000E-3, 7.500000E-3, 1.000000E-2, 1.500000E-2, 2.000000E-2, 2.530000E-2, 3.765000E-2, 5.000000E-2, 7.500000E-2, 1.000000E-1, 1.500000E-1, 2.000000E-1, 2.750000E-1, 3.500000E-1, 5.000000E-1, 7.500000E-1, 1.000000E+0, 1.500000E+0, 2.000000E+0, 2.750000E+0, 3.500000E+0, 5.000000E+0, 7.500000E+0, 1.000000E+1, 1.125450E+1, 1.250900E+1, 1.313625E+1, 1.376350E+1, 1.407713E+1, 1.439075E+1, 1.470438E+1, 1.501800E+1, 1.541700E+1, 1.581600E+1, 1.607750E+1, 1.633900E+1, 1.660050E+1, 1.686200E+1, 1.738500E+1, 1.790800E+1, 1.895400E+1, 2.000000E+1, 2.187500E+1, 2.375000E+1, 2.562500E+1, 2.750000E+1, 3.125000E+1, 3.500000E+1, 3.875000E+1, 4.250000E+1, 5.000000E+1, 6.250000E+1, 7.500000E+1, 8.750000E+1, 1.000000E+2, 1.250000E+2, 1.500000E+2, 1.750000E+2, 2.000000E+2, 2.375000E+2, 2.750000E+2, 3.500000E+2, 4.250000E+2, 5.000000E+2, 6.250000E+2, 7.500000E+2, 1.000000E+3, 1.250000E+3, 1.500000E+3, 2.000000E+3, 2.499999E+3, 3.750000E+3, 5.000000E+3, 1.000000E+4, 2.000000E+7 ]\n",
    "y = [ 4.632900E+0, 3.782743E+0, 3.275961E+0, 2.793748E+0, 2.476392E+0, 2.071907E+0, 1.691707E+0, 1.465067E+0, 1.196231E+0, 1.035972E+0, 8.834888E-1, 7.831367E-1, 6.552317E-1, 5.350119E-1, 4.633490E-1, 3.783474E-1, 3.276796E-1, 2.794733E-1, 2.477507E-1, 2.073234E-1, 1.693338E-1, 1.466949E-1, 1.198536E-1, 1.038636E-1, 9.240960E-2, 7.587376E-2, 6.594569E-2, 5.401993E-2, 4.693537E-2, 3.857382E-2, 3.362565E-2, 2.896063E-2, 2.592687E-2, 2.213011E-2, 1.868993E-2, 1.675174E-2, 1.467738E-2, 1.367516E-2, 1.308271E-2, 1.310050E-2, 1.433955E-2, 2.024836E-2, 3.734890E-2, 5.940236E-2, 1.143543E-1, 1.793393E-1, 3.246526E-1, 4.752279E-1, 7.577459E-1, 1.362666E+0, 2.825472E+0, 5.579952E+0, 2.753086E+0, 1.459760E+0, 8.542251E-1, 5.483105E-1, 3.776269E-1, 2.072622E-1, 1.293821E-1, 6.317288E-2, 3.681692E-2, 1.779573E-2, 1.027333E-2, 6.595747E-3, 4.546226E-3, 2.484924E-3, 1.535209E-3, 1.027866E-3, 7.285479E-4, 4.119763E-4, 1.988113E-4, 1.133302E-4, 7.168982E-5, 4.870401E-5, 2.595335E-5, 1.570763E-5, 1.034298E-5, 7.231695E-6, 4.584339E-6, 3.118320E-6, 1.663789E-6, 1.007539E-6, 6.636192E-7, 3.750892E-7, 2.357914E-7, 1.136674E-7, 6.466135E-8, 4.082132E-8, 1.978276E-8, 1.128921E-8, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0 ]\n",
    "bx = [ 1.000000E-5, 2.525864E-5, 4.051727E-5, 7.103455E-5, 1.320691E-4, 2.541382E-4, 4.982764E-4, 9.865527E-4, 1.963105E-3, 3.916211E-3, 7.822422E-3, 1.563484E-2, 3.125969E-2, 6.250938E-2, 1.250088E-1, 2.500075E-1, 5.000050E-1, 1.000000E+0, 2.000000E+0, 3.000000E+0, 6.004550E+0, 9.009101E+0, 1.051138E+1, 1.201365E+1, 1.276479E+1, 1.351593E+1, 1.389149E+1, 1.426706E+1, 1.464263E+1, 1.483042E+1, 1.501820E+1, 1.541700E+1, 1.581580E+1, 1.611904E+1, 1.642229E+1, 1.672553E+1, 1.702878E+1, 1.763527E+1, 1.824176E+1, 1.884825E+1, 1.945474E+1, 2.066772E+1, 2.188070E+1, 2.309368E+1, 2.551964E+1, 2.794560E+1, 3.037157E+1, 3.522349E+1, 4.007541E+1, 4.492733E+1, 5.463118E+1, 6.433502E+1, 7.403887E+1, 9.344656E+1, 1.128542E+2, 1.322619E+2, 1.710773E+2, 2.098927E+2, 2.487081E+2, 3.263388E+2, 4.039696E+2, 4.816003E+2, 6.368618E+2, 7.921234E+2, 9.473849E+2, 1.257908E+3, 1.568431E+3, 1.878954E+3, 2.500000E+3, 2.000000E+7 ]\n",
    "by = [ 4.632876E+0, 2.915074E+0, 2.301629E+0, 1.738285E+0, 1.274850E+0, 9.190335E-1, 6.563642E-1, 4.664953E-1, 3.307429E-1, 2.342282E-1, 1.658142E-1, 1.174048E-1, 8.319960E-2, 5.907527E-2, 4.211612E-2, 3.027376E-2, 2.213002E-2, 1.675174E-2, 1.367516E-2, 1.303467E-2, 1.602203E-2, 2.816382E-2, 4.436741E-2, 8.561916E-2, 1.355851E-1, 2.512017E-1, 3.760417E-1, 6.227468E-1, 1.200274E+0, 1.798477E+0, 2.826879E+0, 5.579952E+0, 2.754493E+0, 1.331291E+0, 7.351212E-1, 4.553009E-1, 3.062847E-1, 1.633433E-1, 1.002465E-1, 6.726200E-2, 4.798796E-2, 2.764116E-2, 1.776215E-2, 1.227086E-2, 6.747704E-3, 4.195057E-3, 2.825205E-3, 1.496005E-3, 9.050186E-4, 5.968394E-4, 3.065413E-4, 1.815307E-4, 1.178342E-4, 5.920103E-5, 3.454811E-5, 2.219209E-5, 1.099476E-5, 6.359301E-6, 4.059740E-6, 1.995277E-6, 1.148328E-6, 7.306063E-7, 3.575302E-7, 2.052251E-7, 1.303393E-7, 6.363998E-8, 3.648019E-8, 2.314767E-8, 1.128927E-8, 0.000000E+0 ]\n",
    "\n",
    "\n",
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "ax.plot(x, y, \".\", label=\"legacy\")\n",
    "ax.plot(bx, by, \"-\", label=\"modern\")\n",
    "\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xlabel(\"Neutron Energy (eV)\")\n",
    "ax.set_ylabel(\"Cross Sections (b)\")\n",
    "ax.set_xlim( 1E-5, 1E4)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Triple resonance test\n",
    "This also utilizes a file with three resonances that @whaeck created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdbd0a5ee50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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acz5evbPSKTwyUuPhC2t/E7un0lJJZGQkKSkpQfwECjRR1F8t2tPi3FsYv7EPcuwI50Z8x1977KL1Dwth02zrmHb9vL2oLoH2aWTtPKg9qVS94nI6ONQ+kv0HW1JUbNi7by93V9OYDSd3oT1eEeV7/I0Z2jW2LmmiqMdOrqIaSmunw6q/3bPxRC+qJU/B4icojUlge0lv1rvP5lVbXyZPvFD/kFS90IIS9se0oLDoCE05XG1j9lX92oJ30lffKTx8x1FMy8yhZW//3WxV8GiiqOdOqaISgTY9rcf5v4OS/ZD9OduWvM9Fh5cwNnIRx4yd3R/2h4yrrGqqVqnh+wDqjHekqICD9gSKiaWFFAXQmO0uf2rzmdnAdxyFeHQcRV3S+SUauthW0PcaCke9xDmel7n22IO8bn5CgsmHOffCv9PgPwNg7v3WlCPu0nBHrM4gWbkFlB3aw+p9ERSZGOIjjzJpVC//jdnbTqyFXeY+sTa2b3KJwOg4ijqkJYpGwuV08PrEc8nM6UFa6gSaOB2wPwc2z7MWZ1oxGZb/B6LjoMtwtrU6jwXuvpzdvYv+KlMhs3LL97jkMHtNHE04QmRZMY/OrHwxouMcTU5Mme0xJ7rF+h5vp6za66jg0UTRiJxSTdUqFTJutx5HCyFnEWyeQ+l3c0hZP52bjPD1l135vv9oOgy8Clr3rHb1MaVq4tx2VlN0Pi1wUEjzAAbcHSw5MXW/vYqBdTbjrtHAPXV6NFGcKaKbw1mXw1mXM7n5ZuZ9PpdhttUMt62hQ9aTkPUktEiy2jS6XWKNJI9sEu6oVQPXJ876ku/ZrQv52YdoKkdpGlHmt43CEXOiROE7sM63MTsSN3Z7NW0dKmg0UZyBMjon8uzCLqwrS+VFuYZ3fpZKv8NfWb2ovnkbVr0CEU2sGW+7jWBtbAZLdkdpt1tVY9nbcugCDOzVg0NNS2AdPDK8nd9/RwcPnxhrEYm7vETh25gdIW6udmkX2bqiieIMVHECw35OB9Ad0m+wVuzLXWoljc1zYPOn9AXsHidfLEwn9oobOCt9qK6zoaqVlVvA9M9W8Bcb3P7hDnrbChlkh9fnr6JTSteq2yiiT1R/+pYofAfcRYqbnu3jQv4ZlEUTxRmq0gkMASJjrIkJu1wElz7BW7M+Y0fmdIbZ1nCbfIR95oewIME7F9VI6HwhxLSo+w+g6r3MnHwcngNggx/dzWntaQZ2aOE+6LdtYeuPBeXPI3GzbtfBU46xGTcPf6KN2XVFE4Wqmgjd+wzg0a/cvFx6OQkRJUy7sJjU/Uut0eHfvAW2CHCeY7VrdLuErKJWOjpcAVYJYMPCQxSaJhh7NIVilQASI4r8ti1EGJ85nSjjePniq617Tmqj0MbsuqOJQvlVsZoq1ekAbgR3GeSt8FZPzYO5f4K5f6KVaUe0O41/L3Bx18034EppHe6PoMLE5XTQpu0RCgra8fBPetOzZQ+YBr/JaEVHP1/u3RJiYLP1PFLK6OWtYspwnii52nFXP3BPBY0mClWtytfZ8JYknOfAxY9CwXYWz5qK2TyHn9vnMVFmc/TNf0L3i63SRteLoWlCeD6ACous3AKa785mm6ctj85cz9QJA0FsdIwu8XtecUlx+fMon8bs9KRm5fs7xkXy8BD/A/dU8GiiUMHhSKbp+XcwflM6kaUlnB+xgUe65ZG4YxFs+AgQSOp/ovttm946ZqORy9y6j5vZwxemL6VuD5nbCugb7WBT9jaOdql6nqbebU90y25ic58oNfjMKrDvUAkv6YC7OqOJQgXNydVUF5J4fJ2NH7+xqqc2z4EFf7YeLTqwt91QvorsT/u0S0jv3D7c4asgO6+dhyZyjJ2mDZERNhyxUeQejiGveIffGWRtbt91J07M+4TPfjtl2kZRhzRRqKCqdJ2N9mnWY+gfoXA3bJlHwdefEPvdB4ySqRz59v840PF8WvYbZZU44gJfUEXVX/2aWr2XBqSnM7q/9QNij2lJohygtLTqL/nMLT/S1fvc5jP537c79tLHuz8Cjw64q0OaKFTdat4G0n/OWwcH8+yWdfSX77jItpqr9q2DWQtgFla11PEqqg4usNmrvayqh/ZtAeDyoedCvJUQdi5sxUDZUGVDdFZuASuzf+Dn3m+maPuJqqe1238sTxSR6IC7uqSJQoVFRmo8z0ZEs7ysD6ukH32uHYSr6b4T62ws/Rcs+QfExltjNrqOYE20i2V5pdr1toHYvWUVrWxNWFvYEle8VdqM6dKd1jnLeOiyHpWXJnLysZsTbRGdHSdmiE1vG13+PMrmKV+nQoWeJgoVFhW73VpfGq0gsRucexccLoDs+SdGiH8zjd7GzmHTnc8Wuoi++iZ693VV+z4qPLJyC3Bv+Io8k8T4V1YwdWIGALOy3UyyuXl+1nK6tWt5SrLISI3ne/uJdom8/EOMP96eEX9iNoBLeyZYC3mpOlHvE4WIpAL3A3HGmKvDHY8KnipHhwM0cUCfq62Hx80HMz5kz6qPGWZbw722N2D6G7Ao1TvQbyR0OgcidH2C+iJz6z5+Ti4zPIOtHk/eNSV2uluBDRLdeytto3A5Hbh7J8IGazvCnGi0bhG5p7ztYt+BQ+zM1RXu6kpIJ+wRkVdFZI+IrKuw/xIR2SQi2SJyr79rGGNyjDETQhmnqudsdpLThvOMXMdlpX/nQvez7Mh4DFp1hpWvwOuj4YlUeOfnsGYqFO0lK7eA5xZmk5VbUP31VdANSSyihZTwnUkub4/ISI3nR1tbAFIj9lTZRrFo3bby7RgpLe8x9dy8r8v3b/1+N+OnZOr/3zoS6hLFa8B/gNeP7xARO/AccDGQB6wUkRmAHXi8wvk3G2P2hDhG1QBUrKrq5HQAd8GxYsj5wlqcafNc2DgDg2A3nTniTuPPC9J5YMK1uJJbhfsjnFGa71oCQKs+w5k6+EQ32EduHAVv3MNvXVF0qKKNItpzuPwnbIemhknDe1FQcowot7V/v2lGE45q99g6FNJEYYxZLCLJFXYPBLKNMTkAIvI2MNoY8zgwqrbvJSK3ArcCdOrUqbaXUfVYpVVVUU2hx0+shzHw41pWzH2L6JzP+K39fX4v71H01tPQ5yfedTaGQFRseD7AGSIrt4CCZR9jI5HJ64Uhg0+8lt65PTRvRwfPj5Wem5Eaz9cLT0wzfrikkEdnrmfSqF4U2K1xFPkmjlg5qlN41KFwzBXdAdjps53n3VcpEYkXkReBNBG5r6rjjDGTjTH9jTH9ExMTgxetajhEoF0/Iobdy7XmL2Qce577PLdT2s4F374P066FJ1LgzathxctwYAeAVlMF2YrsHxnEeha7+1JaZsrbJ8o5UqBgW+UnA7HmCIeMlcybGKvkUFByjJsGWH/X0XGtaRPjrnbtbRU84WjMrmzeBlPVwcaYfOD20IWjGpuTq6kuxeF0QNkx2LHMqp7a9CnM/gxm/4ESRw9W5XdnUdnZPGfvzhsTz9Evn9N0YbMdNJfDLDV9K/3Vvy+qPU12LOK7ShqjM3Py6cARCkwzmnH4pJJDh2yDERvfHYok2RTpmtl1KOAShYg4RKSXiKSKyOmURPKAjj7bScCu07ieUqdwOR38aliXE18iEVHWin2XPA53rYE7V8GIP7Pf05SbZSbvRT3CUtutNJt1B6z7AA4fCGf4DVr3ohUYsZM+dPQp03Rk5Rbw5mYbTY/tY+KURaeU4jJS42lmO0oxTSghmrPbRpdfY3d+PkeIocgTRaycaKNQoee3RCEiccCvgHFAFLAXiAHaiEgm8LwxZmEN33Ml0FVEUoDvgWuB62oauFK1JgIJXSGhK7s7XM9VU+aT4fmG4fY1jDq4HN6fDWKHToNPjBBP6KqTGAbCGNg8B0kawC0Xp53ycmZOPlvc7cAGndx5pzRGu5wODrWPoqi4JfYjhTgiSzmClWC2rstliERRYqJpgrZR1KXqqp7ex+qxdL4x5qSfWCLiAn4uIqnGmFcqO1lEpgFDgQQRyQMeMsa8IiJ3AnOxejq9aoxZf5qfQ6lacTkdvDhxOJk5Z9Mx9XdEdGwB32edGCH+2YPWo2Un6HIx2XEZLDjaA1fXjlrlUYlNK+bSffc6cgf/BWclr2ekxjPTZr3SMyKv0i96e2kxRSYa99EItuTt5t4pmYxNT+IcU0wRTThMDE3lqLZR1CG/icIYc7Gf17KArGrOH1fF/tnA7EACVCrUTulN1XGg9Rg+CQ7stLreZs/H/fU0upS9QidjJ2tJD/IGXEHSgCug9Vla2sD61b9/9lO0phmjlybxSo9T2yBcTgd/vnk0ZW/cx929SmlT4fWs3AJa7d3DZo+TzhJDU45QWubBAK1sRRSY5pQQTQzHeGzmt9pGUUdq0kYxRkT+KSL/EJGrQhmUUvVGy44wYCKMm8bkwQsYf+x+XnVfisMcImnV4/DCYPhnT/j4Ttjw8RndtrFh/dcMZxVT3cMpLIussv3AlZLA0VbdOfb9ulPaKDJz8onnIHtNHAdpSgspxm4TxqYn0S/eYI91UIjVIyq6rFjbKOpIQL2eROR5oAswzbvrNhG5yBjzq5BFplQ9M7BLW55Z1IfMsl78S67n3Wuc9D2yCrI/hw0zYM0bVttGx0HQZTgbmg5i4cE2ZHROPCN+9V5y8D3KsDHVPcJv+0FWbgHb97RiiKxm/JTlTJ04uPz+DHY2o4WUkG9acNA0xSm7y0trEUcP4Gni5GCRtdJdvK1Y2yjqSKDdY4cAvY0xBkBE/gd8G7KolKqHKo4O7+t0AD0h/QZr9bW8VZD9mZU4FjxGTyDRxPHlon44ho0lddAVENtIR4h/+z6Jm6ayp+cvuL51ht8ZfjNz8tntTmFs5CLalO0+qUE74sh+APYRxwHTjH62rbjdHj5YncdZRfmscQv7PU0BiJPiSq+vgi/QRLEJ6ATkerc7AmtDEpFS9ViVExnaI8E52HoMn8Src79i/eLpXGBbyxDbahxfLIYv7rbW1+hyEXS+EDqkW+c1cBtXLaTrrF9yuM1AWo99il9VMzljRmo8j9h6ANDfvhlH7PAT18reSl+wShTSlDisqqdodwmxcpS9xkogAM09hTqFRx2prnvsJ1iD4eKAjSKywrs9CFgW+vCUapj69ejGE0uH8lHZBURHwAejm3BW4VdWaeOLv8MXf4OoZuA8B1IugJQLyDqaROa2gga13sbaDRto+8mN/EAc1+yayH++L8bl9J8oXE4H4y4bSeGnD+OSTScNnGttOwRQXvXURI4RKWW0FavtZ69pyQG8VU8RJVr1VEeqK1E8VSdRKNXIVKymOsvpAC6EYfdByX7YvsSazHDbYtgyD4DOphl7PD2ZubA30VePp3cfV/3uTXWsmLazb6IJR7n+2H3soVnAv/D3H3aTZbqRLpvLl0UFmLviW4bZYD9WYzZA87JDfLH6R26LhL3SitGDesIauPucRO/kkCrUqksUi4+3S1RFRKS6Y5Q6E1VZTRXbCnqOth4Ah3bx2ez3OLj+cwbb1nOprIDpr8Jn7cpLGySfb43lqC+Jw+OBD28nsWgTt3vuYSsdazQALiM1niULu3O37V1aSjGO2Cgyc/Jp49mLR4Q9piV7sdpz2toLaIOVSHablkQ2s95j14+72KtrUtSJ6hLFQhH5APjYGLPj+E4RiQLOA34BLMSaTlwpVRst2tNq8A38en03So+5SY3Yy5TzS3AeXGWt8rf2Heu45u2h0yDoNJgNET1ZdDCRQZ3b1O0XpbsMNnwEXz4DP65FRvyZWztcT9+TViqsnsvpIP+cyyHzXS6QNTw6szmTRvUiwb6D7aYtnsgmXHn+QFgGI5LclO6yVhvYZWtDXLOmFJsY1mdv58mtmadME6KCr7pEcQlwMzDNO+XGAawpPOzAPOBpY8zXfs5XSgXg5Kqqc3Ee/+IzBvZsgNxlsGM57PgK1n9ITyDVRPLdF8ns6XkOrbtnQPs0SOgGNnvwAzxaxM75LxH3zcu0OPqD9T5XvgD9xuESqdUX9ZaoHvQ1DkbaVjKj7DwKSo5xeewuNtCVSUN70T7uKCyD3Xk59LXt5gfTimMmkvW7DjKEZsRJsa5JUUeqG5l9BHgeeF5EIoEE4HDF6TyUUqev0qoqEWjTy3oMvAWA/326hI+7YGoAAB1iSURBVNVL59Bbcuhry6Hl5vdg4/+s4yNjoW0fSOwOCd0hoRvfHmvL4j0xlY7nyMotqLBuuQ+PBw7lQdb/KPvqZToeO8hKT3deNeOZOOoOXMmn15Cc0TmRzxcN5GrbAuIijpEYeYzmh/NYUnYOU2au5+q09nQ10aTID/SS7WzydMTtMRjgIM1oSZHO91RHAp5m3BhTCvwQwliUUgHo3bM3jy8rYmbZOUSKjanXD8DVLB92rbEeP6yF72ZBibWwZB/gLGNj3xctKU5Moml8EjRrww+lsSz7ejdHPTbmL4ykXXoC7dkHB3daU5cc+h7cxwAhN2Eo/7drCFmebtgFem8rOO1E4XI6aDpqIjGz5/JS4gd8s82aMWi9x0mp8eARG2voxkX21bQnn9nuQURG2BibnoQjry1RhQeYNFzne6oL4ViPQil1Gir2qLK+KBOsUkS/a08cWJzP9M8WsHJlJh1kL23kAAPdx2h6YAfs/Io2JQX82uYpn8jHfC3QvC3EdbSqsXpeYT1PHcaB4njWT8nEbjxB/RVf3GYAL3lGc9v+j0nOX8whYlltupUnhIiSISRt+Q8Aa+jOpFG9AFi+N5rBskfXpKgjmiiUaoCq7FHlq2k8zrSL+FNWM0rLrC/4qVdmlLd/rMkt4Popy6CslCYRhik3nUN6apvK3y+BSpLT6cvMyecfpdcQH1HA1fbF/L30WoqlGY96Z4Z9aUkv+gGHTRSr3J3ptOsgBSXH8HgctLYX4Ckr1TaKOhDoXE9NsdomPCLSDegBfOqtjlJK1VOVlz5OvPbmxHPKX0uv5ss2oORUQxmp8URF2LmvdCKfuAez1NMbxFBQYq2Pndu0D+OP3cc3ns4cJgbjPWfGwgTsYmgXcUjbKOpAoCWKxcD5IuIA5gOrgJ8B40MVmFIqOPx9wYfiy78mjieyD1bn8X5WFFSo2hqbnsS4VX0pxRBlt2aRdTkdtBhxDnw+hXHdQ9DDS50i0EQhxpgSEZkAPGuMeUJE1oQyMKXUmeF4shqbnnRKycfldDDt1sGn7C9t2haAbzdu5F+bWupYihALOFGIyGCsEsSEGp6rlFLVqqp0U9n+ZXuj6Qm0Yb+OpagDgS5c9BvgPuBDY8x6EUnFGpGtlFJ1Lq1bKkdMJO1lv46lqAMBlQqMMYux2imOb+cAd4UqKKWU8seV3IojLTowNOYoaaO02inUAu311A34A5Dse44x5sLQhKWUUv4dbeEk7uBODoY7kDNAoO0M7wEvAlMAd+jCUUqp6mXlFrBuZyxXy/ZTllNVwRdooigzxrwQ0kiUUipAmTn5/OhuS9PIo8SX7dPG7BALtDH7ExG5Q0TaiUir44+QRqaUUlXISI1nh60DAF0jftTG7BALNFH8ArgHa/nTLO9jVaiCUkopf1xOB78fNwqAMZ0Ohzmaxi+gRGGMSankkRrq4JRSqiqlsW0oNjEc2PEt46dkkpVbEO6QGq1Aez1FAr8ELvDuWgS8pHM9KaXCJXPbfozpRA/ZUb7utrZThEagVU8vAC68ixh5n2vjtlIqbDJS49lEMmfJDmxicMRGhTukRivQXk8DjDH9fLYXiMg3oQhIKaUC4XI6KO17Ls3XzSPJ7Na1KUIo0BKFW0Q6H9/wTuGh4ymUUmG1I8r6Wuop28vnfFLBF2iiuAdYKCKLROQLYAHw+9CFpZRS1evSeyCHTRQu2xad8ymEAp3rab6IdAW6AwJ8Z4w5GtLIlFKqGumpbShsl86oou30+6nO+RQqfhOFiFxojFkgImMqvNRZRDDGTA9hbEopVa3m3S6g+ZKnaN1WVz4Ileru7BCsaqbLK3nNAJoolFLh1SkDjIePZ35MUv/LtFQRAn4ThTHmIe/TR40x23xfE5GUkEWllFIBWkM3+hohd83n/PHreF3tLgQCbcz+oJJ97wczkKqIyFki8qKIvC8iv6yL91RKNRzLdh5lg0lmgGzSnk8h4jdRiEgPERkLxInIGJ/HjUBMdRcXkVdFZI+IrKuw/xIR2SQi2SJyr79rGGM2GmNuB64B+lf7iZRSZ5SM1HiyOIt02xaaR5Rqz6cQqK6NojswCmjJye0UhcAtAVz/NeA/wOvHd4iIHXgOuBjIA1aKyAzADjxe4fybjTF7ROQK4F7vtZRSqpzL6aD5JeOInjeb33X5MdzhNErVtVF8DHwsIoONMctrenFjzGIRSa6weyCQ7V1OFRF5GxhtjHkcKylVdp0ZwAwRmQW8VdkxInIrcCtAp06dahqqUqoBK2o7kGITjT17HuOzndpOEWSBtlHcLiItj2+IiENEXq3le3YAdvps53n3VUpEhorIv0XkJWB2VccZYyYbY/obY/onJibWMjSlVEO0PLeIpZ4+DLV9TWmZW9spgizQjsd9jTEHjm8YYwpEJK2W7ymV7DNVHWyMWYQ1W61SSlUqIzWeDxemM1JW0d2WhyO2b7hDalQCLVHYRKS8HOdd3a62o1vygI4+20nArlpeSymlcDkdpA+/Fo8RRspXPDpzva5PEUSBJop/AMtE5DEReRRrpbsnavmeK4GuIpIiIlHAtcCMWl5LKaUA+METR6bpyRW2L7X6KcgCXeHudWAssBvYC4wxxrxR3XkiMg1YDnQXkTwRmWCMKQPuBOYCG4F3jTHra/sBlFIKrOqnWZxLim03aRHbtZtsENWk+qgVUGyM+a+IJIpISsXR2hUZY8ZVsX82fhqmlVKqplxOB/brf4V72n95rs9W2mivp6AJqEQhIg8BfwTu8+6KBN4MVVBKKVUbZ3dLxt59JG12zAKPLpkTLIG2UVwFXAEUAxhjdgHNQxWUUkrVWp+fQtFuPv7oHW3QDpJAE8UxY4zB241VRJqGLiSllKq91TGDKDJNOLbmbcZPydRkEQSBJop3vQPeWorILcDnwMuhC0sppWpn+Y5iPvUM5FLbV0SVFWvvpyAItNfTU1izxX4AdAMmGWOeDWVgSilVGxmp8bzHRTSTI4yJ/FJ7PwVBoCUKjDGfAX/DGkOxP2QRKaXUaXA5HfxxwvXsadaDu1ssJnPrPq1+Ok3VTTM+U0R6e5+3A9YBNwNviMjddRCfUkrVmCu5FSX9bqRlUTZffD5D2ypOU3UlihRjzPG1JG4CPjPGXA4MwkoYSilVL82V8zhkYrnO/rkuaHSaqksUpT7Ph+MdJGeMKQQ8oQpKKaVOV/+uSXxohvAT21e0lQIcsVHhDqnBqi5R7BSRX4vIVUA6MAdARJpgDbpTSql6yeV00HLYXdgw/MI2WycKPA3VJYoJQC/gRuBnPlONZwD/DWFcSil12vJozSxPBtfZ59Ok7JBWP9WS30RhjNljjLndGDPaGDPPZ/9Cb5dZpZSqtzJS43mV0TSTI9wQOV+7ytZSwN1jlVKqoXE5HUya+DNyW53LnbGf42ofE+6QGiRNFEqpRs3ldOC84k9EHsmHNTqXaW1oolBKNX7OcyFpAEcXP8PzC77TRu0aCnSa8SdEpIWIRIrIfBHZJyLXhzo4pZQKChGyu91CdNFONs9/XQfg1VCgJYoRxphDwCisNa+7AfeELCqllAqyuWVpbPIkcaf9Q9xlZdoDqgYCTRTHx0z8BJhmjNG5npRSDUpG50SeN1fTxbaLqyKXaQ+oGgg0UXwiIt8B/YH5IpIIHAldWEopFVwup4MbJtzF3mbdeTRuFq6kZuEOqcEIdJrxe4HBQH9jTCnWSnejQxmYUkoFmys5nsTLHyWmMJcF7/xL2ykCFGhj9k+BMmOMW0QewFovu31II1NKqRDIih7I16YLPTa9wI1TlmiyCECgVU8PGmMKReQ8YCTwP+CF0IWllFKhkbltP/8o/SntJZ+fms+0UTsAgSYKt/e/lwEvGGM+BnQqRqVUg5ORGs9Ke1+We3rya/t08vP3aqmiGoEmiu+9a2ZfA8wWkeganKuUUvWGy+lg6sTBZHX/PXEU0/rr53RcRTUC/bK/BpgLXOKdQbYVOo5CKdVAuZwOpP3ZfOg5j5vsc0gs261VUH4E2uupBNgKjBSRO4HWvrPJKqVUQ5ORGs+z/AwD3BP5no6r8CPQXk+/AaYCrb2PN0Xk16EMTCmlQsnldPCPiaNY5/w5V9iW4orYFu6Q6i0xxlR/kMhaYLAxpti73RRYbozpG+L4aqV///5m1apV4Q5DKdUQHDkE/06D1mfBLz4BkXBHFDYikmWM6V9xf6BtFMKJnk94n5+5d1Mp1XjEtIBh98H2JWQvfZ/nFmZrw3YFgSaK/wJficjDIvIwkAm8ErKolFKqLqX/gsNxnbF9Poln5q3XXlAVBNqY/U/gJmA/UADcZIz5VygDU0qpOmOPZEHSnaTKLsbbPqO0zKO9oHxEVHeAiNiAtcaY3sDq0IeklFJ1r+2AK1my7jV+F/E+8zznai8oH9WWKIwxHuAbEelUB/EopVRYuJJb4Rj7T2JtpXzcYz4upyPcIdUb1ZYovNoB60VkBdbMsQAYY64ISVRKKRUGvfv2hx/vIGHZv3n/48tIOXuoJgwCTxSPhDQKP0RkKPAYsB542xizKFyxKKUavzWpt9LhyzfomvUo166K4s2J55zxycJv1ZOIdBGRc40xX/g+AIO1JKpfIvKqiOwRkXUV9l8iIptEJFtE7q3mMgYoAmICeU+llDody3Ye5fGycfSz5TDas1Abtam+jeJfQGEl+0u8r1XnNeAS3x0iYgeeAy4FegLjRKSniPQRkZkVHq2BJcaYS4E/EsaSjVLqzJCRGs+ntvNZ5enGPRHvcG6HQCteGq/qEkWyMWZtxZ3GmFVAcnUXN8YsxupS62sgkG2MyTHGHAPeBkYbY741xoyq8NjjbUwHq1tudFXvJSK3isgqEVm1d+/e6kJTSqlKHZ9ddmv/h2glhbRb8/QZPwivulQZ4+e1JrV8zw7ATp/tPGBQVQeLyBisxZJaAv+p6jhjzGRgMlhTeNQyNqWUwuV04HKOYm/xdcRveJ2Z3/TgWXsyUydmnJHtFdWVKFaKyC0Vd4rIBCCrlu9Z2dQfVX6xG2OmG2NuM8b8TBuylVJ16eP4mzlELI9GvEpZWdkZ215RXYnibuBDERnPicTQH2t1u6tq+Z55QEef7SRgVy2vpZRSIZPWvTNPLr2ex20vcm3kYjJSzwt3SGHhN1EYY3YD54jIMKC3d/csY8yC03jPlUBXEUkBvgeuBa47jesppVRIuJwOuPmPfP/hCh4+/A4RCX8Md0hhEehcTwuNMc96HwEnCRGZBiwHuotInohMMMaUAXdirZi3EXjXGLO+NsErpVSouZJb0WH8C0SUFcO8B8MdTliEtN+XMWZcFftnA7ND+d5KKRU0rXvAOXfB0n/C2ddByvnhjqhOBTrNuFJKndkuuAdaOjn80W94Yf6GM6q7rCYKpZQKRFQsW/o/TJODWyle+PQZtWaFJgqllArQvNK+zHYP5E77h7Qr++GM6S6riUIppQKUkRrP37iRUiJ4JOo1MlJahTukOqGJQimlAuRyOnh64k/4usuvuEC+wVW0KNwh1QlNFEopVQMup4Pzx/8J2vXj2Kw/8vJnaxp9W4UmCqWUqimbnY39H8NespeYxX9p9A3bmiiUUqoWFhzqwP/cl/Bz+2f0dW9s1A3bmiiUUqoWMlLjeVZ+xk6TyOORLzO4U9NwhxQymiiUUqoWXE4HUyYOZU3fSXSWXbi/eKrRVj9polBKqVpyOR106H85H3nO5+zc//LIlHcbZbLQRKGUUqchMyefx0rHc4hYHpHJfLV1T7hDCjpNFEopdRoyUuMpjmjJY2U3kGbLZtTRWeEOKeg0USil1Gmw1tjOoOvwmziYNIxOq5+CAzvCHVZQaaJQSqnT5HI6+NWFXYm7+llrxyd3g6lyhecGRxOFUkoFS8uOcNFDsHU+2xa+xnMLsxtF47YmCqWUCqYBEylKTKPlFw/y33krG8WobU0USikVTDY7nzjvoykl/CniTUrLPA1+1LYmCqWUCrJufQbysrmSMfalDItYS0ZqfLhDOi2aKJRSKshcTgeDb/wr+2NTeK7FG7jaRoY7pNOiiUIppUIgPbUtra59iejiXTD/0XCHc1o0USilVKh0GgQDb8WsmMz0j95rsI3amiiUUiqE1nS7i+9NAmevfpCbpyxukMlCE4VSSoXQsp1HuLf0FlJtP3CHea9B9oDSRKGUUiGUkRrPKntf3nYPY6J9Jhc23xnukGpME4VSSoXQ8bmgCi94CHfTNiQv/T9emL+hQVVBaaJQSqkQczkd3HJxGrnn/JUmBzZTuvCJBjViWxOFUkrVkXml/ZjuPo9f2mfQxZ3TYNorNFEopVQdyUiN5+/cyAGa8ffIyWQktwh3SAHRRKGUUnXE5XTw/MSLWNn7AXrJdlw7Xw93SAGJCHcASil1JnE5HeC8FVgGX/wdelwGrc8Kd1h+aYlCKaXC4dInIaoZfPwr8LjDHY1fmiiUUiocmiXCT56E77P48s1H63UPKE0USikVJlnNL2S+ceHa+hx/mvJhvU0W9T5RiMj5IvKiiEwRkWXhjkcppYIlc9t+7j92M8eI5DF5icyte8MdUqVCmihE5FUR2SMi6yrsv0RENolItojc6+8axpglxpjbgZnA/0IZr1JK1aWM1HgORMTz57LrGWj7jsuPfRrukCoV6hLFa8AlvjtExA48B1wK9ATGiUhPEekjIjMrPFr7nHodMC3E8SqlVJ05Pr2Hc/itHGx/Pp2y/g4F28Md1ilC2j3WGLNYRJIr7B4IZBtjcgBE5G1gtDHmcWBUZdcRkU7AQWPMoareS0RuBW4F6NSp0+kHr5RSdcDldFhdZtOfh+cHc+jdO3ij6zNkdE6w9tcD4Wij6AD4Tp+Y593nzwTgv/4OMMZMNsb0N8b0T0xMPM0QlVKqjrXsRK7rXlr88CXfz3+hXs0FFY5EIZXsM/5OMMY8ZIzRhmylVKM2M3Ikyzy9+FPEVBLLdtebuaDCkSjygI4+20nArjDEoZRS9UpG50QeNLchGP4WNYWMlFbhDgkIT6JYCXQVkRQRiQKuBWaEIQ6llKpXXE4HT0y8gpXdfsu58i3xW97huYXZYa+CCnX32GnAcqC7iOSJyARjTBlwJzAX2Ai8a4xZH8o4lFKqoXA5HQwd90cOtR1MwtJHmDbvy7C3V4Q0URhjxhlj2hljIo0xScaYV7z7ZxtjuhljOhtj/hLKGJRSqsGx2fio030IHv4SMYXSMndY2yvq/chspZQ6E/Xq1Zd/mOsYYl/LtZFfkJEaH7ZYNFEopVQ95HI6uOzmB/k+zsWj0dNwtSwOWyyaKJRSqp5yJcfT4RevYMcNn/wGjN+RBCGjiUIppeqzVilw0SOQ/Tl8PTUsIWiiUEqp+m7ARHCeS9mn9/LanC/rvAeUJgqllKrvbDa+7f9XSo8eJenLB+q8u6wmCqWUagAW72vGu56hnG/7ltIyT512l9VEoZRSDUBGajxHJQaAyAhbnXaXDek040oppYLD5XTQoV977OuEqTdn1OkU5JoolFKqgWjbIgZsUufrVGjVk1JKKb80USillPJLE4VSSim/NFEopZTySxOFUkopvzRRKKVUg1L3EwOKCdNshKEkIoXApnDH0QAkAPvCHUQDofcqMHqfAlNf75PTGJNYcWdjHUexyRjTP9xB1HciskrvU2D0XgVG71NgGtp90qonpZRSfmmiUEop5VdjTRSTwx1AA6H3KXB6rwKj9ykwDeo+NcrGbKWUUsHTWEsUSimlgkQThVJKKb80USillPLrjEsUIjJURJaIyIsiMjTc8dRXInKW9x69LyK/DHc89ZWIpIrIKyLyfrhjqY/0/gSmvv+9NahEISKvisgeEVlXYf8lIrJJRLJF5N5qLmOAIiAGyAtVrOEUjPtkjNlojLkduAZoMAODaiJI9ynHGDMhtJHWLzW5b2fi/Tmuhvepfv+9GWMazAO4AEgH1vnsswNbgVQgCvgG6An0AWZWeLQGbN7z2gBTw/2Z6ut98p5zBbAMuC7cn6k+3yfvee+H+/PUx/t2Jt6f2t6n+vz31qCm8DDGLBaR5Aq7BwLZxpgcABF5GxhtjHkcGOXncgVAdCjiDLdg3SdjzAxghojMAt4KXcThEeR/T2eMmtw3YEPdRld/1PQ+1ee/twZV9VSFDsBOn+08775KicgYEXkJeAP4T4hjq09qep+Gisi/vfdqdqiDq0dqep/iReRFIE1E7gt1cPVYpfdN788pqrpP9frvrUGVKKogleyrchShMWY6MD104dRbNb1Pi4BFoQqmHqvpfcoHbg9dOA1GpfdN788pqrpPi6jHf2+NoUSRB3T02U4CdoUplvpM71Ng9D7Vjt63wDTI+9QYEsVKoKuIpIhIFHAtMCPMMdVHep8Co/epdvS+BaZB3qcGlShEZBqwHOguInkiMsEYUwbcCcwFNgLvGmPWhzPOcNP7FBi9T7Wj9y0wjek+6aSASiml/GpQJQqllFJ1TxOFUkopvzRRKKWU8ksThVJKKb80USillPJLE4VSSim/NFGoBkFEjIj8w2f7DyLycC2v1VJE7ghacKde/2ER+V5EvvZ5tAzV+9WEiKSJyBQ/rzcVkXwRiauw/yMRuUZERonII6GPVNUnmihUQ3EUGCMiCUG4Vkug0kQhIvYgXB/gaWPM2T6PA6d7wSDF9ifg2apeNMYUA/OAK33eNw44D2tq9VnAFSISG4RYVAOhiUI1FGXAZOC3FV8QkUQR+UBEVnof53r3Pywif/A5bp132ue/AZ29v/Sf9M7cuVBE3gK+9R77O+/x60Tkbu++ZBHZKCIvi8h6EZknIk0C/QAicqOITBeROSKyRUSe8HlthIgsF5HVIvKeiDTz7t8uIpNEZCnwUxEZICJrvcc+eXxRHLFWbTzb53pfikjfCu/fHOhrjPnGu91UrMV1VorIGhEZ7T10GtbUEsddBcwxxpQYa4TuInTK9TOKJgrVkDwHjK9YLQI8g/ULfgAwFqiyasXrXmCr95f+Pd59A4H7jTE9RcQF3AQMAjKAW0QkzXtcV+A5Y0wv4ID3/SrzW59qp4U++88Gfoa1ENLPRKSjt5T0AHCRMSYdWAX8zuecI8aY84wxbwP/BW43xgwG3D7HTAFuBBCRbkC0MWZthZj6A76rrd0PLPDet2HAkyLSFJgDuEQk3nvctVjJ47hVwPlVfG7VCDWGacbVGcIYc0hEXgfuAg77vHQR0FOkfAbnFt5fzzWxwhizzfv8POBDbzUMIjId64txBrDNGPO197gsILmK6z1tjHmqkv3zjTEHvdfdADixqsJ6Al96P0MU1hxBx73jPb4l0NwYs8y7/y1O/LJ/D3hQRO4BbgZeq+S92wF7fbZHYFUjHS91xQCdjDEbRWQGcLWIfICV3Ob5nLcHaF/F51aNkCYK1dD8C1iN9cv6OBsw2BjjmzwQkTJOLjXH+Lluse+pfo476vPcDQRc9VTF+RHe9/vMGDOumtiqjMsYUyIin2GtllbVusuHOfkeCDDWGLOpkmOnYZVyBPjYGFPq81oMJydq1chp1ZNqUIwx+4F3gQk+u+dhzcgJgE9d/XasNYsRkXQgxbu/EPBX4lgMXCkisd6qmKuAJcGIvwqZwLki0sUba6y3+ugkxpgCoFBEMry7rq1wyBTg38BK732qaCPQxWd7LvBr8RZjfKrXABZiVbP9ipOrnQC6cXIVlmrkNFGohugfgG/vp7uA/t5G3g2cWFHtA6CViHwN/BLYDOWr0n3pbah+suLFjTGrsapuVgBfAVOMMWtqGKNvG8XXcurayb7vtxerfWGaiKzFShw9qjh8AjBZRJZj/do/6HOdLOAQJ5e2fN/nOyDOp1ruMSASWOttFH/M51gP1v2Lx0qcvoZh9X5SZwidZlypBkREmhljirzP7wXaGWN+491uj9UjqYf3i76y838LFBpjqmvwr+r92wBvGWOG1+Z81TBpiUKphuUybwllHVYD+58BROQGrNLP/VUlCa8XOLmdpKY6Ab8/jfNVA6QlCqWUUn5piUIppZRfmiiUUkr5pYlCKaWUX5oolFJK+aWJQimllF+aKJRSSvn1/87d/lTlAD0aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [ 1.000000E-5, 2.525864E-5, 4.051727E-5, 7.103455E-5, 1.320691E-4, 2.541382E-4, 4.982764E-4, 9.865527E-4, 1.963105E-3, 3.916211E-3, 7.822422E-3, 1.563484E-2, 3.125969E-2, 6.250938E-2, 1.250088E-1, 2.500075E-1, 5.000050E-1, 1.000000E+0, 2.000000E+0, 3.000000E+0, 6.004550E+0, 9.009101E+0, 1.051138E+1, 1.201365E+1, 1.276479E+1, 1.351593E+1, 1.389149E+1, 1.426706E+1, 1.464263E+1, 1.483042E+1, 1.501820E+1, 1.541700E+1, 1.581580E+1, 1.607316E+1, 1.633052E+1, 1.658789E+1, 1.684525E+1, 1.735998E+1, 1.787470E+1, 1.890415E+1, 1.993360E+1, 2.096305E+1, 2.199250E+1, 2.405141E+1, 2.611031E+1, 2.816921E+1, 2.919866E+1, 2.971339E+1, 3.022811E+1, 3.074284E+1, 3.100020E+1, 3.125756E+1, 3.151493E+1, 3.164361E+1, 3.177229E+1, 3.190097E+1, 3.202965E+1, 3.209399E+1, 3.215833E+1, 3.222267E+1, 3.228701E+1, 3.232700E+1, 3.236699E+1, 3.242508E+1, 3.248318E+1, 3.254127E+1, 3.259937E+1, 3.271556E+1, 3.283174E+1, 3.294793E+1, 3.306412E+1, 3.329650E+1, 3.352888E+1, 3.376126E+1, 3.422602E+1, 3.469078E+1, 3.515554E+1, 3.608506E+1, 3.794410E+1, 3.980313E+1, 4.166217E+1, 4.259169E+1, 4.352121E+1, 4.445072E+1, 4.538024E+1, 4.584500E+1, 4.630976E+1, 4.654214E+1, 4.677452E+1, 4.700690E+1, 4.723928E+1, 4.753400E+1, 4.782872E+1, 4.797839E+1, 4.812806E+1, 4.842740E+1, 4.872673E+1, 4.902607E+1, 4.962474E+1, 5.022342E+1, 5.082209E+1, 5.142077E+1, 5.261812E+1, 5.381547E+1, 5.501282E+1, 5.740751E+1, 5.980221E+1, 6.219691E+1, 6.698631E+1, 7.177571E+1, 7.656510E+1, 8.614390E+1, 9.572269E+1, 1.053015E+2, 1.244591E+2, 1.436167E+2, 1.627742E+2, 2.010894E+2, 2.394046E+2, 2.777198E+2, 3.543501E+2, 4.309805E+2, 5.076108E+2, 6.608715E+2, 8.141322E+2, 9.673930E+2, 1.273914E+3, 1.580436E+3, 1.886957E+3, 2.500000E+3, 2.000000E+7 ]\n",
    "y = [ 5.456376E+0, 3.433203E+0, 2.710721E+0, 2.047255E+0, 1.501444E+0, 1.082383E+0, 7.730244E-1, 5.494059E-1, 3.895215E-1, 2.758481E-1, 1.952684E-1, 1.382468E-1, 9.795092E-2, 6.952310E-2, 4.952719E-2, 3.554709E-2, 2.590596E-2, 1.949035E-2, 1.571469E-2, 1.479125E-2, 1.749478E-2, 2.961498E-2, 4.585171E-2, 8.714487E-2, 1.371084E-1, 2.526195E-1, 3.772588E-1, 6.234100E-1, 1.199173E+0, 1.795227E+0, 2.820036E+0, 5.584260E+0, 2.767689E+0, 1.481546E+0, 8.725722E-1, 5.627137E-1, 3.890599E-1, 2.151008E-1, 1.353575E-1, 6.751729E-2, 4.061684E-2, 2.754927E-2, 2.042888E-2, 1.399204E-2, 1.282895E-2, 1.730690E-2, 2.524751E-2, 3.341497E-2, 4.841369E-2, 8.032977E-2, 1.117985E-1, 1.683890E-1, 2.860562E-1, 3.994356E-1, 5.980030E-1, 9.928742E-1, 1.949962E+0, 3.022606E+0, 5.186197E+0, 1.009249E+1, 2.004363E+1, 2.389296E+1, 1.955624E+1, 1.046739E+1, 5.662441E+0, 3.397331E+0, 2.228435E+0, 1.153175E+0, 6.988931E-1, 4.677194E-1, 3.348493E-1, 1.963055E-1, 1.294944E-1, 9.233600E-2, 5.471537E-2, 3.710559E-2, 2.755499E-2, 1.829473E-2, 1.288561E-2, 1.348110E-2, 1.886286E-2, 2.486216E-2, 3.581221E-2, 5.825644E-2, 1.151481E-1, 1.831905E-1, 3.366529E-1, 4.966762E-1, 7.984573E-1, 1.448431E+0, 3.031956E+0, 6.042620E+0, 3.011921E+0, 1.837726E+0, 1.186784E+0, 5.884222E-1, 3.443448E-1, 2.243462E-1, 1.161193E-1, 7.061015E-2, 4.738542E-2, 3.398220E-2, 1.992923E-2, 1.310348E-2, 9.281725E-3, 5.374598E-3, 3.514927E-3, 2.483731E-3, 1.434659E-3, 9.355661E-4, 6.582490E-4, 3.755134E-4, 2.411175E-4, 1.668573E-4, 9.213908E-5, 5.745359E-5, 3.875799E-5, 2.054278E-5, 1.241980E-5, 8.180644E-6, 4.189923E-6, 2.474860E-6, 1.602966E-6, 8.026503E-7, 4.672842E-7, 2.996286E-7, 1.480846E-7, 8.551487E-8, 5.453118E-8, 2.676175E-8, 0.000000E+0 ]\n",
    "bx = [ 1.000000E-5, 1.500000E-5, 2.000000E-5, 2.750000E-5, 3.500000E-5, 5.000000E-5, 7.500000E-5, 1.000000E-4, 1.500000E-4, 2.000000E-4, 2.750000E-4, 3.500000E-4, 5.000000E-4, 7.500000E-4, 1.000000E-3, 1.500000E-3, 2.000000E-3, 2.750000E-3, 3.500000E-3, 5.000000E-3, 7.500000E-3, 1.000000E-2, 1.500000E-2, 2.000000E-2, 2.530000E-2, 3.765000E-2, 5.000000E-2, 7.500000E-2, 1.000000E-1, 1.500000E-1, 2.000000E-1, 2.750000E-1, 3.500000E-1, 5.000000E-1, 7.500000E-1, 1.000000E+0, 1.500000E+0, 2.000000E+0, 2.750000E+0, 3.500000E+0, 5.000000E+0, 7.500000E+0, 1.000000E+1, 1.125450E+1, 1.250900E+1, 1.313625E+1, 1.376350E+1, 1.407713E+1, 1.439075E+1, 1.470438E+1, 1.501800E+1, 1.541700E+1, 1.581600E+1, 1.607750E+1, 1.633900E+1, 1.660050E+1, 1.686200E+1, 1.738500E+1, 1.790800E+1, 1.895400E+1, 2.000000E+1, 2.152989E+1, 2.305978E+1, 2.611955E+1, 2.764944E+1, 2.917933E+1, 2.994428E+1, 3.032675E+1, 3.070922E+1, 3.109169E+1, 3.128293E+1, 3.147416E+1, 3.166540E+1, 3.185663E+1, 3.195225E+1, 3.204787E+1, 3.214349E+1, 3.219130E+1, 3.223910E+1, 3.232700E+1, 3.241490E+1, 3.247281E+1, 3.253072E+1, 3.258863E+1, 3.264653E+1, 3.276235E+1, 3.287816E+1, 3.299398E+1, 3.310979E+1, 3.334141E+1, 3.357304E+1, 3.380467E+1, 3.426792E+1, 3.473118E+1, 3.519443E+1, 3.612093E+1, 3.797394E+1, 3.982695E+1, 4.167997E+1, 4.260648E+1, 4.353298E+1, 4.445949E+1, 4.538599E+1, 4.584925E+1, 4.631250E+1, 4.654413E+1, 4.677575E+1, 4.700738E+1, 4.723900E+1, 4.753400E+1, 4.782900E+1, 4.796469E+1, 4.810038E+1, 4.837175E+1, 4.864313E+1, 4.891450E+1, 4.945725E+1, 5.000000E+1, 5.078125E+1, 5.156250E+1, 5.312500E+1, 5.468750E+1, 5.625000E+1, 5.937500E+1, 6.250000E+1, 6.875000E+1, 7.500000E+1, 8.125000E+1, 8.750000E+1, 1.000000E+2, 1.125000E+2, 1.250000E+2, 1.500000E+2, 1.750000E+2, 2.000000E+2, 2.375000E+2, 2.750000E+2, 3.500000E+2, 4.250000E+2, 5.000000E+2, 6.250000E+2, 7.500000E+2, 1.000000E+3, 1.250000E+3, 1.500000E+3, 2.000000E+3, 2.499999E+3, 3.750000E+3, 5.000000E+3, 1.000000E+4, 2.000000E+7 ]\n",
    "by = [ 5.456371E+0, 4.455113E+0, 3.858240E+0, 3.290317E+0, 2.916557E+0, 2.440174E+0, 1.992399E+0, 1.725474E+0, 1.408850E+0, 1.220107E+0, 1.040521E+0, 9.223310E-1, 7.716910E-1, 6.301016E-1, 5.457002E-1, 4.455885E-1, 3.859137E-1, 3.291373E-1, 2.917747E-1, 2.441593E-1, 1.994139E-1, 1.727483E-1, 1.411314E-1, 1.222953E-1, 1.088017E-1, 8.931932E-2, 7.762032E-2, 6.356410E-2, 5.521116E-2, 4.534784E-2, 3.950679E-2, 3.399492E-2, 3.040611E-2, 2.590608E-2, 2.181221E-2, 1.949035E-2, 1.697173E-2, 1.571469E-2, 1.489280E-2, 1.477179E-2, 1.586116E-2, 2.169219E-2, 3.881965E-2, 6.090854E-2, 1.158834E-1, 1.808350E-1, 3.259606E-1, 4.762433E-1, 7.580435E-1, 1.361000E+0, 2.818634E+0, 5.584260E+0, 2.766277E+0, 1.467138E+0, 8.589155E-1, 5.518209E-1, 3.805778E-1, 2.097813E-1, 1.317922E-1, 6.564310E-2, 3.948724E-2, 2.313795E-2, 1.621430E-2, 1.283358E-2, 1.530312E-2, 2.501814E-2, 3.894362E-2, 5.268685E-2, 7.728314E-2, 1.279516E-1, 1.763095E-1, 2.602269E-1, 4.253018E-1, 8.215112E-1, 1.266629E+0, 2.188870E+0, 4.530205E+0, 7.181802E+0, 1.215345E+1, 2.389296E+1, 1.175018E+1, 6.271591E+0, 3.700414E+0, 2.395460E+0, 1.663888E+0, 9.286808E-1, 5.895380E-1, 4.069165E-1, 2.978080E-1, 1.797734E-1, 1.208113E-1, 8.726199E-2, 5.258746E-2, 3.604385E-2, 2.696548E-2, 1.807775E-2, 1.286065E-2, 1.351563E-2, 1.894919E-2, 2.498816E-2, 3.600272E-2, 5.856883E-2, 1.157375E-1, 1.840720E-1, 3.380652E-1, 4.984963E-1, 8.006951E-1, 1.450434E+0, 3.029094E+0, 6.042620E+0, 3.009060E+0, 1.918839E+0, 1.280571E+0, 6.605458E-1, 3.948568E-1, 2.605776E-1, 1.368223E-1, 8.383726E-2, 4.857687E-2, 3.164660E-2, 1.650407E-2, 1.013337E-2, 6.865525E-3, 3.766663E-3, 2.386872E-3, 1.212236E-3, 7.335174E-4, 4.908325E-4, 3.506315E-4, 2.030192E-4, 1.310457E-4, 9.079180E-5, 5.002283E-5, 3.106754E-5, 2.087280E-5, 1.270522E-5, 8.408039E-6, 4.332728E-6, 2.568942E-6, 1.668224E-6, 9.284077E-7, 5.778076E-7, 2.751724E-7, 1.554243E-7, 9.766266E-8, 4.705685E-8, 2.676173E-8, 0.000000E+0, 0.000000E+0, 0.000000E+0, 0.000000E+0 ]\n",
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "ax.plot(x, y, \".\", label=\"legacy\")\n",
    "ax.plot(bx, by, \"-\", label=\"modern\")\n",
    "\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xlabel(\"Neutron Energy (eV)\")\n",
    "ax.set_ylabel(\"Cross Sections (b)\")\n",
    "ax.set_xlim( 1E-5, 1E4)\n",
    "ax.legend()"
   ]
  }
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